As digital innovation has exploded, so has the amount of data that enterprises create. In an effort to wrangle this data into a more usable format, organizations have tried to collect and unify it in a single repository. But it is unrealistic to think that you will be able to collect and keep every piece of data in one place, all the time.
Data fabric offers a unique approach to harnessing this explosion of data across the enterprise. Data fabric is an architecture layer and tool set that connects data across disparate systems to create a unified view. With a data fabric you can leave your current data sources in place and let the data fabric connect them. You access the data as if it’s local, while the data fabric keeps source systems updated in real-time.
Data fabrics do this with a virtualized data layer that sits over your current systems to connect them together. For this reason, a data fabric approach allows you to combine business data in entirely new ways. This solution to longtime data integration woes can significantly speed up development work and help your business meet digital transformation goals.
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A data fabric is used to connect disparate data sets sitting inside organizational data silos. It’s common across enterprises to have large, inflexible data silos in many business units. That’s challenging when cross-departmental use cases require you to use data across these silos to achieve business outcomes. These desired outcomes could be building powerful new applications, driving greater efficiencies through process improvements, modernizing legacy solutions, or strengthening governance and compliance.
This is where a data fabric comes into play. In the past, organizations would consider undergoing a massive data warehouse migration to collect structured data into a single place or using a data lake, where you could dump massive amounts of unstructured data. In both cases, you’d be relying on ETL/ELT tools that end up duplicating large amounts of data and lead to stale data when processing in batches.
[ Want to demystify data terms and explain them to others? Read our related article: Data Fabric vs. Data Mesh vs. Data Lake. ]
A data fabric takes the unique approach of keeping data where it is. No migration or futile attempts to get everything into one spot. Instead, a data fabric sits on top of these systems and stitches them together into a centralized platform with a virtualized data layer. This virtualized model provides you with real-time data from connected systems and the ability to create, read, update, and delete (CRUD) any part of the data set from wherever you’re leveraging it.
The term “data fabric” can refer to both the tool itself or the architectural layer where you use a data orchestration toolset. Both are correct, but let’s add some important context.
When talking about data fabric as an architectural layer, you are usually referring to the back-end engine(s) that powers the data connection. This architecture is doing things like keeping track of the connections to data sources, syncing data, tuning it, optimizations, storage, etc.
When talking about data fabric as a tool, you are typically referring to the front end of the technology, where users are able to configure data sources and systems and create a data model. From this frontend, users are able to physically see the complete view of their organization’s data.
Your organization’s records can now travel anywhere across your technology landscape in a single, unified view.
Regardless of whether you’re talking about it as a tool or an architecture, the end result for the organization is the same. Different sources of data that were separated and isolated are now connected. Your people have real-time data to make better, faster decisions. Your developers no longer wait weeks or months for migrations before creating new applications.
Data fabrics can seem a little “meta,” even after you understand what they are. It often helps to see how they work in practice. Let’s get into a few examples of how an organization would use a data fabric in real life.
Customer support teams that troubleshoot issues with customers need to have a vast amount of data at their fingertips to handle a wide range of potential problems. However, customer information, product orders, shipping, and warranty data all live in silos across CRMs, ERPs, custom relational database systems, and legacy systems. To make matters more complicated, as enterprises grow and acquire other organizations, they begin to have multiple instances of CRMs, ERPs, etc.
This creates a big headache for employees, who have to sift through systems to find the information they need to resolve customer inquiries. A data fabric architecture takes this burden off of support agents and gives them a complete view of their customer in a single interface.
Migrating all of this data is nearly impossible and would quickly become a time-intensive and costly project. A data fabric presents a much easier solution to the problem by leaving data where it is and connecting systems with a virtual data layer.
Enterprise organizations must manage the process of onboarding individual workers. From new employees to contractors to vendors, this workflow requires data across contracts, financial, and HR systems and any other internal tools that might be needed.
In order to manage access to sensitive data and ensure regulatory compliance, you’d need a complete view of how and where this data is being used. A data fabric centralizes data access management across systems. This allows the IT department to see where data is being accessed by every person from the moment they are onboarded.
These real-world examples illustrate why data fabric is more than a hot trend in data management—it’s here to stay. Organizations seeking new speed and agility for software developers and line of business teams can realize real value from a data fabric approach. The benefits can be summarized into four main categories:
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